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20242026
most citedTrustLLM: Trustworthiness in Large Language Models

54 citations · 67 across the 19 of their papers we have counts for

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8 papers · 1 filter

cs.LG2024

A Hierarchical Language Model For Interpretable Graph Reasoning

Sambhav Khurana, Xiner Li, Shurui Gui +1

Large language models (LLMs) are being increasingly explored for graph tasks. Despite their remarkable success in text-based tasks, LLMs' capabilities in understanding explicit gra…

cs.AI2024★ 1 cited

Geometry Informed Tokenization of Molecules for Language Model Generation

Xiner Li, Limei Wang, Youzhi Luo +5

We consider molecule generation in 3D space using language models (LMs), which requires discrete tokenization of 3D molecular geometries. Although tokenization of molecular graphs…

q-bio.BM2024★ 1 cited

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Cong Fu, Xiner Li, Blake Olson +2

Structure-based drug design (SBDD) is crucial for developing specific and effective therapeutics against protein targets but remains challenging due to complex protein-ligand inter…

cs.CL2024

Eliminating Position Bias of Language Models: A Mechanistic Approach

Ziqi Wang, Hanlin Zhang, Xiner Li +6

Position bias has proven to be a prevalent issue of modern language models (LMs), where the models prioritize content based on its position within the given context. This bias ofte…

cs.CL2024★ 3 cited

A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Yu Zhang, Xiusi Chen, Bowen Jin +4

In many scientific fields, large language models (LLMs) have revolutionized the way text and other modalities of data (e.g., molecules and proteins) are handled, achieving superior…

cs.LG2024★ 1 cited

Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency

Yuchao Lin, Jacob Helwig, Shurui Gui +1

We consider achieving equivariance in machine learning systems via frame averaging. Current frame averaging methods involve a costly sum over large frames or rely on sampling-based…

cs.LG2024★ 1 cited

Active Test-Time Adaptation: Theoretical Analyses and An Algorithm

Shurui Gui, Xiner Li, Shuiwang Ji

Test-time adaptation (TTA) addresses distribution shifts for streaming test data in unsupervised settings. Currently, most TTA methods can only deal with minor shifts and rely heav…

cs.CL2024★ 54 cited

TrustLLM: Trustworthiness in Large Language Models

Yue Huang, Lichao Sun, Haoran Wang +67

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs prese…